Using textual Eye detection helps to identify eyes, other facial features with reference to eyes, face detection and geometrical methods of face recognition. It also helps as a preprocessing aid in achieving the large objective of face recognition. Multiple approaches for eye detection can be found in the literature. Some of the methods include modeling of geometrical structure, deformable templates and search methods; image processing approaches like multiple variants of edge detection, thresholding, filtering approaches, etc; pattern recognition approaches such as adaptive boosting (AdaBoost), k-means clustering, principal component analysis; projection functions and their many variants; hybrid methods, etc. Eye detection itself may not usually be the final objective. It helps to meet larger goals such as face detection or face recognition. In view of this, the activity is expected to be accurate, efficient with minimum requirement of iteration or search for solution. Eye detection methods have to handle one more of the following constraints as part of the solution or through preprocessing steps, viz., face rotation, scale, illumination, skin color, one or both eyes closed, etc. Limitations of existing methods include large amount of time and convergence to local minima while localizing eyes. It is desirable to arrive at a method that does not take exponential order of computation time and are devoid of false positives. Accordingly, there is a need for a technique that, proposes a method detected eyes irrespective of reasonable rotation, scaling, and change in illumination.